How to configure a MySQL Datasource

This guide shows how to connect to a MySql Datasource. Great Expectations uses SqlAlchemy to connect to MySQL.

Steps

Show Docs for V2 (Batch Kwargs) API

Prerequisites: This how-to guide assumes you have already:

  1. Install the required modules

    If you have not already done so, install required modules for connecting to MySql.

    pip install sqlalchemy
pip install PyMySQL

  2. Run datasource new

    From the command line, run:

    great_expectations datasource new

  3. Choose “Relational database (SQL)”

    What data would you like Great Expectations to connect to?
    1. Files on a filesystem (for processing with Pandas or Spark)
    2. Relational database (SQL)
: 2

  4. Choose MySQL

    Which database backend are you using?
    1. MySQL
    2. Postgres
    3. Redshift
    4. Snowflake
    5. BigQuery
    6. other - Do you have a working SQLAlchemy connection string?
: 1

  5. Give your Datasource a name

    When prompted, provide a custom name for your Snowflake data source, or hit Enter to accept the default.

    Give your new Datasource a short name.
 [my_mysql_db]:

  6. Enter connection information

    When prompted, provide a custom name for your new Datasource, or hit Enter to accept the default.

    Next, we will configure database credentials and store them in the `my_mysql_db` section
of this config file: great_expectations/uncommitted/config_variables.yml:

What is the host for the MySQL connection? [localhost]:
What is the port for the MySQL connection? [3306]:
What is the username for the MySQL connection? []: root
What is the password for the MySQL connection?:
What is the database name for the MySQL connection? []: test_ci
Attempting to connect to your database. This may take a moment...

  7. Save your new configuration

    Great Expectations will now add a new Datasource 'my_mysql_db' to your deployment, by adding this entry to your great_expectations.yml:

  my_mysql_db:
    credentials: ${my_mysql_db}
    data_asset_type:
      class_name: SqlAlchemyDataset
      module_name: great_expectations.dataset
    class_name: SqlAlchemyDatasource
    module_name: great_expectations.datasource

The credentials will be saved in uncommitted/config_variables.yml under the key 'my_mysql_db'

Show Docs for V3 (Batch Request) API

Prerequisites: This how-to guide assumes you have already:

To add a MySql datasource, do the following:

  1. Install the required modules.

    If you have not already done so, install required modules for connecting to MySql.

    pip install sqlalchemy
pip install PyMySQL

  2. Run datasource new

    From the command line, run:

    great_expectations --v3-api datasource new

  3. Choose “Relational database (SQL)”

    What data would you like Great Expectations to connect to?
    1. Files on a filesystem (for processing with Pandas or Spark)
    2. Relational database (SQL)
: 2

  4. Choose MySQL

    Which database backend are you using?
    1. MySQL
    2. Postgres
    3. Redshift
    4. Snowflake
    5. BigQuery
    6. other - Do you have a working SQLAlchemy connection string?
: 1

  5. You will be presented with a Jupyter Notebook which will guide you through the steps of creating a Datasource.

MySql SimpleSqlalchemyDatasource Example.

Within this notebook, you will have the opportunity to create your own yaml Datasource configuration. The following text walks through an example.

Parameters can be set as strings, or passed in as environment variables. In the following example, a yaml config is configured for a SimpleSqlalchemyDatasource with associated credentials passed in as strings.

datasource_name = "my_mysql_datasource"
config = f"""
    name: {datasource_name}
    class_name: SimpleSqlalchemyDatasource
    credentials:
      drivername: mysql+pymysql
      host: YOUR_MYSQL_HOST
      port: YOUR_MYSQL_PORT
      username: YOUR_MYSQL_USERNAME
      password: YOUR_MYSQL_PASSWORD
      database: YOUR_MYSQL_DB_NAME
    introspection:
      whole_table:
        data_asset_name_suffix: __whole_table
    """

Note: Additional examples of yaml configurations for various filesystems and databases can be found in the following document: How to configure Data Context components using test_yaml_config

  1. Test your config using ``context.test_yaml_config``.

    context.test_yaml_config(
    yaml_config=config
)

    When executed, test_yaml_config will instantiate the component and run through a self_check procedure to verify that the component works as expected.

    The resulting output will look something like this:

    Attempting to instantiate class from config...
Instantiating as a DataSource, since class_name is SimpleSqlalchemyDatasource
Successfully instantiated SimpleSqlalchemyDatasource

Execution engine: SqlAlchemyExecutionEngine
Data connectors:
    whole_table : InferredAssetSqlDataConnector

    Available data_asset_names (1 of 1):
        imdb_100k_main__whole_table (1 of 1): [{}]

    Unmatched data_references (0 of 0): []

    This means all has gone well and you can proceed with configuring your new Datasource. If something about your configuration wasn’t set up correctly, test_yaml_config will raise an error.

  2. Save the config.

    Once you are satisfied with the config of your new Datasource, you can make it a permanent part of your Great Expectations configuration. The following method will save the new Datasource to your great_expectations.yml:

    sanitize_yaml_and_save_datasource(context, config, overwrite_existing=False)

    Note: This will output a warning if a Datasource with the same name already exists. Use overwrite_existing=True to force overwriting.

    Note: The credentials will be stored in uncommitted/config_variables.yml to prevent checking them into version control.

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